[HN Gopher] Bayesian Data Analysis, Third edition (2013) [pdf]
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Bayesian Data Analysis, Third edition (2013) [pdf]
Author : ibobev
Score : 165 points
Date : 2025-09-28 17:23 UTC (5 hours ago)
(HTM) web link (sites.stat.columbia.edu)
(TXT) w3m dump (sites.stat.columbia.edu)
| moscoe wrote:
| Related course materials here:
| https://sites.stat.columbia.edu/gelman/book/
| mcdonje wrote:
| I'm a fan of the stats blog hosted by Columbia that Gelman is the
| main contributor to: https://statmodeling.stat.columbia.edu
| dpflan wrote:
| Thanks for sharing, any particular articles that had last
| impact on you?
| mcdonje wrote:
| idk about impact, but here are a couple I liked:
|
| - https://statmodeling.stat.columbia.edu/2025/08/25/what-
| writi...
|
| - https://statmodeling.stat.columbia.edu/2025/09/04/assemblin
| g...
| cubefox wrote:
| Here are the articles that were popular on HN over the years:
|
| https://hn.algolia.com/?q=statmodeling.stat.columbia.edu
| 1u15 wrote:
| Beyond "power pose": Using replication failures and a better
| understanding of data collection and analysis to do better
| science
| https://statmodeling.stat.columbia.edu/2017/10/18/beyond-
| pow...
|
| You need 16 times the sample size to estimate an interaction
| than to estimate a main effect
| https://statmodeling.stat.columbia.edu/2018/03/15/need16/
|
| Debate over effect of reduced prosecutions on urban
| homicides; also larger questions about synthetic control
| methods in causal inference.
| https://statmodeling.stat.columbia.edu/2023/10/12/debate-
| ove...
|
| Bayesians moving from defense to offense: "I really think
| it's kind of irresponsible now not to use the information
| from all those thousands of medical trials that came before.
| Is that very radical?" https://statmodeling.stat.columbia.edu
| /2023/12/23/bayesians-...
| asdev wrote:
| Looking for more self study statistics resources for someone with
| a CS degree, any other recs?
| fishmicrowaver wrote:
| Probability Theory by Jaynes if you'd like more bayes
| 3eb7988a1663 wrote:
| I think Statistical Rethinking [0] is a far more approachable
| first entry. The author posts his video lectures on Youtube
| which are excellent and should be watched with the book. The
| book gets way less into the mathematical weeds than other
| texts, so a working statistician would require something
| deeper.
|
| [0] https://en.wikipedia.org/wiki/Statistical_Rethinking
| sebg wrote:
| 2024 videos / lectures on github here ->
| https://github.com/rmcelreath/stat_rethinking_2024
| CuriouslyC wrote:
| Start here:
|
| https://www.inference.org.uk/itprnn/book.pdf
|
| It's a little dated now but it connects Bayesian statistics
| with neural nets and information theory in an elegant way.
| mamonster wrote:
| Start with statistics by David Freedman. It is very
| approachable as an introduction, not too theory heavy, can get
| a handle on all of the "main" issues. Afterwards, you have 2
| options:
|
| 1) Do you want "theoretical" knowledge(math background
| required)? If so, then you need to get a decent mathematical
| statistics book like Casella-Berger. I think a good US CS
| degree grad could handle it, but you might need to go a bit
| slow and google around/ maybe fill in some gaps in
| probability/calculus.
|
| 2)Introduction to Statistical Learning is unironically a great
| intro to "applied" stats. You have most of the "vanilla"
| models/algorithms, theoretical background behind each but not
| too much, you can follow along with the R version and see how
| stuff actually works and exercises that vary in difficulty.
|
| With regards to Gelman and Bayesian data analysis, I should
| note that in my experience the Bayesian approach is 1st year MS
| /4th year of a Bachelors in the US. It's very useful to know
| and have in your toolbox but IMO it should be left aside until
| you are confident in the "frequentist" basics.
| atdt wrote:
| I am interested in this topic, but this textbook is too daunting
| for me. What I'd love is a crash course on Bayesian methods for
| the working systems performance engineer. If you, dear reader,
| happen to be familiar with both domains: what would you include
| in such a course, and can you recommend any existing resources
| for self-study?
| esafak wrote:
| https://github.com/CamDavidsonPilon/Probabilistic-Programmin...
|
| https://www.oreilly.com/library/view/bayesian-methods-for/97...
| JHonaker wrote:
| My go to for teaching statistics is Statistical Rethinking.
| It's basically a course in how to actually thing about
| modeling: what you're really looking for is analyzing a
| hypothesis, and a model may be consistent with a number of
| hypotheses, figuring out what hypotheses any given model
| implies is the hard/fun part, and this book teaches you that.
| The only drawback is that it's not free. (Although there are
| excellent lectures by the author available for free on YouTube.
| These are worth watching even if you don't get the book.)
|
| I also recommend Gelman's (one of the authors of the linked
| book) Regression and Other Stories as a more approachable text
| for this content.
|
| Think Bayes and Bayesian Methods for Hackers are introductory
| books from a beginner coming from a programming background.
|
| If you want something more from the ML world that heavily
| emphasizes the benefits of probabilistic (Bayesian) methods, I
| highly recommend Kevin Murphy's Probabilistic Machine Learning.
| I have only read the first edition before he split it into two
| volumes and expanded it, but I've only heard good things about
| the new volumes too.
| huijzer wrote:
| Yep 100% came here to say the same. Helped me a lot during
| the PhD to get a better understanding of statistics.
| kianN wrote:
| This is my favorite book on statistics. Full stop. The author
| Andrew Gelman created a whole new branch of Bayesian statistics
| with both his theoretical work on hierarchical modeling while
| also publishing Stan to enable practical applications of
| hierarchical models.
|
| It took me about a year to work through this book on the side
| (including the exercises) and it provided the foundation for
| years of fruitful research into hierarchical Bayesian models.
| It's a definitely not an introductory read, but for any looking
| to advance their statistical toolkit, I cannot recommend this
| book highly enough.
|
| As a starting point, I'd strongly suggest the first 5 chapters
| for an excellent introduction to Gelman's modeling philosophy,
| and then jumping around the table of contents to any topics that
| look interesting.
| SilverElfin wrote:
| Is there a good book that covers statistics as it is applied to
| testing - like for medical research or as optimization or
| manufacturing or whatever?
| kianN wrote:
| This book is very relevant to those fields. There is a common
| choice in statistics to either stratify or aggregate your
| dataset.
|
| There is an example in his book discussing efficacy trials
| across seven hospitals. If you stratify the data, you lose a
| lot of confidence, if you aggregate the data, you end up just
| modeling the difference between hospitals.
|
| Hierarchical modeling allows you to split your dataset under
| a single unified model. This is really powerful for
| extracting signal for noise because you can split your
| dataset according to potential confounding variables eg the
| hospital from which the data was collected.
|
| I am writing this on my phone so apologies for the lack of
| links, but in short the approach in this book is extremely
| relevant of medical testing.
| crystal_revenge wrote:
| The key insight to recognize is that within the Bayesian
| framework hypothesis testing _is_ parameter estimation. Your
| certainty in the outcome of the test is your posterior
| probability over the test-relevant parameters.
|
| Once you realize this you can easily develop very
| sophisticated testing models (if necessary) that are also
| easy to understand and reason about. This dramatically
| simplifies.
|
| If you're looking for a specific book recommendation
| _Statistical Rethinking_ does a good job covering this at
| length and _Bayesian Statistics the Fun Way_ is a more
| beginner friendly book that covers the basics of Bayesian
| hypothesis testing.
| kianN wrote:
| I might checkout Statistical Rethinking given how
| frequently it is being recommended!
|
| Edit: Haha I just found the textbook and I'm remembering
| now that I actually worked through sections of it back when
| I was working through BDA several years back.
| pyyxbkshed wrote:
| What is a book / course on statistics that I can go through
| before this so that I can understand this?
| kianN wrote:
| I don't mean for the bar to sound too high. I think working
| through khan academy's full probability, calculus and linear
| algebra courses would give you a strong foundation. I worked
| through this book having just completed the equivalent
| courses in college.
|
| It's just a relatively dense book. There's some other really
| good suggestions in this thread, most of which I've heard
| good things about. If you have a background in programming,
| I'd suggest Bayesian Methods for Hackers as a really good
| starting point. But you can also definitely tackle this book
| head on, and it will be very rewarding.
| crystal_revenge wrote:
| _Bayesian Statistics the Fun Way_ is probably the best place
| to start if you 're coming at this from 0. It covers the
| basics of most of the foundational math you'll need along the
| way and assumes basically no prerequisites.
|
| After than _Statistical Rethinking_ will take you much deeper
| into more complex experiment design using linear models and
| beyond as well as deepening your understanding of other areas
| of math required.
| 1u15 wrote:
| Regression and Other Stories. It's also co-authored by Gelman
| and it reads like an updated version of his previous book
| Data Analysis Using Hierarchical/Multilevel Models.
|
| Statistical Rethinking is a good option too.
| armcat wrote:
| Can second Regression and Other Stories, it's freely
| available here: https://users.aalto.fi/~ave/ROS.pdf, and
| you can access additional information such as data and code
| (including Python and Julia ports) here:
| https://avehtari.github.io/ROS-Examples/index.html
| ccosm wrote:
| Highly recommend Stats 110 from Blitzstein. Lectures and
| textbook are all online https://stat110.hsites.harvard.edu/
| tomhow wrote:
| Previously:
|
| _Bayesian Data Analysis, Third Edition [pdf]_ -
| https://news.ycombinator.com/item?id=23091359 - May 2020 (48
| comments)
| g9yuayon wrote:
| I can attest how useful Bayesian analysis is. My team recently
| needed to sample from many millions of items to test their
| qualities. The question is that given a certain budget and
| expectation, what's the minimum or maximum number of items that
| we need to sample. There was an elegant solution to this problem.
|
| What was surprising, though, was how reluctant the engineers are
| to learn such basic techniques. It's not like the math was hard.
| They all went through the first-year college math and I'm sure
| they did reasonably well.
| j7ake wrote:
| Is Bayesian data analysis relevant anymore in the era of
| foundation models and big data?
| canjobear wrote:
| Why would it not be? You can use big data and neural nets to
| fit Bayesian models (variational inference).
| j7ake wrote:
| I meant specifically the book, which doe not have any of
| those things you mentioned.
|
| Also nobody fits neural networks and use variation inference
| using any priors that aren't some standard form that makes
| algorithm easy
| mitthrowaway2 wrote:
| Even in this era, there are some problems for which data is
| extremely limited. Those IMO tend to be the problems in which
| Bayesian techniques shine the most.
| canyon289 wrote:
| BDA is THE book to learn Bayesian Modeling in depth rigorously.
| For different approaches there are a number shared here like
| Statistical Rethinking from Richard McElreath or Regression and
| other stories which Gelman and Aki wrote as well.
|
| I also write a book on the topic which is focused a code and
| example approach. It's available for open access here.
| https://bayesiancomputationbook.com
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